Automated vehicle acceptance (AVA) research has grown substantially in the past few years. There is a paucity of research on the role of the big five personality traits on attitudes towards automated vehicles (AVs) and AVA. This is a critical shortcoming given that personality is considered a critical factor explaining technology adoption. Our major theoretical contribution is the integration of the most popular personality measure – the big five – and one of the most influential technology acceptance models – Unified Theory of Acceptance and Use of Technology (UTAUT2). A questionnaire was administered to 9,339 respondents from nine countries to predict the behavioral intention to use conditionally automated vehicles (CondAVs). The original UTAUT2 was extended by trust and driver engagement and the big five personality traits openness, conscientiousness, extraversion, agreeableness, and neuroticism. Structural equation modeling was applied to examine the direct effects of these constructs on behavioral intention and the indirect effects of the personality traits on the independent constructs of the extended UTAUT2. The results have shown positive effects of social influence, trust, and performance expectancy on the behavioral intention to use CondAVs. Most of the hypotheses pertaining to the role of the personality traits on the UTAUT2 constructs were supported, but the effects were relatively small (< 0.25). Our findings support the usefulness of UTAUT2 in evaluating the success of AVs, providing crucial insights into the factors driving the acceptance of CondAVs. The cross-country analysis provides further insights into the role of an individual’s personality for AVA. Our study yields important implications for practitioners. Given the small effect sizes of personality, designing CondAVs around the personalities of their customers during development and commercialization may be ineffective to promote trust and acceptance.

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As vehicles transition between driving automation levels, drivers need to be continually aware of the automation mode and the resulting driver responsibilities. This study investigates the impact of visual user interfaces (UIs) on drivers’ mode awareness in SAE Level 2 automated vehicles. It focuses on their understanding of speed and distance control, steering control, and the hands-on steering wheel requirement presented through UIs. Forty-five UIs were generated, presenting the activation of Lane Keeping Assist (LKA) and Adaptive Cruise Control (ACC) and the hands-on steering wheel requirement. Through an online questionnaire with 1080 respondents with experience of SAE Level 2, the study evaluated how these visual UIs influenced users’ understanding of control responsibilities, information usability, and trust in automated vehicles. The results show a limited role of UI in shaping users’ understanding of control. ACC UIs and LKA UIs had no significant effects, and apparently, the understanding of speed and distance control and steering control was independent of the ACC UI and LKA UI. A large variance in responses regarding the understanding of steering control and speed and distance control indicates confusion caused by mode ambiguity, suggesting that drivers do not well understand how the speed and distance control and steering control task is shared between the driver and the automation. However, the hands-on steering wheel UIs significantly improved the understanding of the hands-on steering wheel requirement. The hands-on steering wheel UI combining the hands on the wheel icon and the text “Keep hands on steering wheel” yielded 94.4% correct understanding and outperformed the UI with hands but without text (87.8% correct) or no UI (82.5% correct). In addition, the variation of visual UI did not affect trust. This study contributes to the understanding and design of visual UIs for effective communication of driver responsibilities in automated vehicles.

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A better understanding of automation disengagements can lead to improved safety and efficiency of automated systems. This study investigates the factors contributing to automation disengagements initiated by human operators and the automation itself by analyzing semi-structured interviews with 103 users of Tesla’s Autopilot and FSD Beta. The factors leading to automation disengagements are represented by categories. In total, we identified five main categories, and thirty-five subcategories. The main categories include human operator states (5), human operator’s perception of the automation (17), human operator’s perception of other humans (3), the automation’s perception of the human operator (3), and the automation incapability in the environment (7). Human operators disengaged the automation when they anticipated failure, observed unnatural or unwanted automation behavior (e.g., erratic steering, running red lights), or believed the automation is not capable to operate safely in certain environments (e.g., inclement weather, non-standard roads). Negative experiences of human operators, such as frustration, unsafe feelings, and distrust represent some of the adverse human operate states leading to automation disengagements initiated by human operators. The automation, in turn, monitored human operators and disengaged itself if it detected insufficient vigilance or speed rule violations by human operators. Moreover, human operators can be influenced by the reactions of passengers and other road users, leading them to disengage the automation if they sensed discomfort, anger, or embarrassment due to the automation’s actions. The results of the analysis are synthesized into a conceptual framework for automation disengagements, borrowing ideas from the human factor's literature and control theory. This research offers insights into the factors contributing to automation disengagements, and highlights not only the concerns of human operators but also the social aspects of this phenomenon. The findings provide information on potential edge cases of automated vehicle technology, which may help to enhance the safety and efficiency of such systems.

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Conditionally automated driving enables drivers to engage in non-driving-related activities, with the responsibility to take over vehicle control upon request. This takeover process increases the risk of collisions, especially when drivers fail to safely complete takeovers within limited time budgets (i.e., the time offered by automation for takeovers). This phenomenon underlines the significance of providing time budgets that sufficiently accommodate drivers' takeover time (i.e., the time required by drivers to resume conscious control of vehicles). Considering that drivers' takeover time varies significantly across scenarios, this study centres on understanding the role of driver perception in takeover time using the Task-Capability Interface (TCI) theory. The TCI theory suggests that drivers adjust their behaviours based on their perceived task demands and driver capabilities. Accordingly, in a driving simulator experiment featuring diverse traffic densities and distractions, we investigated drivers' takeover time while capturing their perceived task demands and capabilities through a takeover-oriented questionnaire based on established instruments. The results show that drivers generally have longer takeover time as their perceived task demand rises, perceived driver capability diminishes, and perceived spare capacity (perceived driver capability minus perceived task demand) decreases. These patterns fluctuate under conditions of low perceived task demand or high perceived driver capability. When both conditions coincide, drivers necessitate a considerably longer time to regain vehicle control. Our findings on takeover time contribute to the development of strategies aimed at predicting drivers' takeover time, optimizing time budgets, fostering human-centred vehicle design, and enhancing the safety of conditionally automated driving.

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The use of partially automated driving systems raises concerns about potential responsibility issues, posing risk to the system safety, acceptance, and adoption of these technologies. The concept of meaningful human control has emerged in response to the responsibility gap problem, requiring the fulfillment of two conditions, tracking and tracing. While this concept has provided important philosophical and design insights on automated driving systems, there is currently little knowledge on how meaningful human control relates to subjective experiences of actual users of these systems. To address this gap, our study aimed to investigate the alignment between the degree of meaningful human control and drivers' perceptions of safety and trust in a real-world partially automated driving system. We utilized previously collected data from interviews with Tesla "Full Self-Driving"(FSD) Beta users, investigating the alignment between the user perception and how well the system was tracking the users' reasons. We found that tracking of users' reasons for driving tasks (such as safe maneuvers) correlated with perceived safety and trust, albeit with notable exceptions. Surprisingly, failure to track lane changing and braking reasons was not necessarily associated with negative perceptions of safety. However, the failure of the system to track expected maneuvers in dangerous situations always resulted in low trust and perceived lack of safety. Overall, our analyses highlight alignment points but also possible discrepancies between perceived safety and trust on the one hand, and meaningful human control on the other hand. Our results can help the developers of automated driving technology to design systems under meaningful human control and are perceived as safe and trustworthy.

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Introduction: Partially automated cars are on the road. Trust in automation and perceived safety are critical factors determining use of automation. Background: Drivers misuse partially automated driving systems. Misuse is associated with mis-calibrated trust in the automation. Research gap: Little is known about the factors impacting the perceived safety when using partial driving automation. Research objective: The main objective of the present study is to provide a comprehensive driver perspective on the psychological aspects of automation use pertaining to trust in automation, perceived safety, and its relationship with use of automation. Method: Semi-structured interviews (n = 103) were conducted with users of partially automated driving systems. Supplemented with content analysis, natural language processing (NLP) techniques were applied to perform automatic text processing. Guided seed-term analysis was conducted to identify the number of occurrences of the subcategories in the dataset. Main results: We identified human operator-related, automation-related, and environmental factors of trust and perceived safety. The identified factors were more strongly associated with perceived safety than with trust. Participants with physical and visual impairments reported to feel safer using the automation compared to driving manually. Neurotic behavior during manual driving contributed to lower trust and perceived safety using the automation. A correct mental model of the capabilities and limitations of the automation did not guarantee proper automation use. A novel conceptual, process-oriented model, titled PTS-a (predicting trust in and perceived safety of automation use), synthesizes the results of the data analysis. Informed by the cognition-leads-to-emotions approach, the model posits that trust as cognition precedes perceived safety as affective construct. Trust and perceived safety determine how human operators (mis-, dis-)use the automation. Future research: We recommend future research to perform experimental studies to identify cognitive-related thoughts and beliefs pertaining to trust in automation and perceived safety to contribute to the operationalization of these constructs, and unravel the nature of their relationship.

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The resistance towards automated vehicles (AVs) is little understood. The main objective of this study is to examine the resistance towards AVs, identifying the factors explaining resistance. Comments submitted by residents of California to the California Public Utilities Commission (CPUC) on the fared deployment of AVs were analyzed. In total, we identified four main themes, and twenty-nine sub-themes. We developed a conceptual framework for resistance that explains resistance by individual and vehicle characteristics, the direct and indirect consequences of use, reactions of others, and external events. AVs were considered incompetent, and unpredictable, violating traffic rules, blocking traffic, not explicitly engaging in communicating with other road users, and causing conflict situations. Respondents questioned the effectiveness of AVs in meeting today's transportation-related challenges, and feared the indirect negative consequences of the deployment of AVs for traffic safety, flow efficiency, transition towards sustainable mobility, environmental efficiency, privacy, economy, social equity, livability of cities, and humanity. Respondents perceived a low responsibility of stakeholders involved in the manufacture, deployment, and regulation of AVs given a lack of accountability, and legal liability. Moreover, they reported a limited involvement of local residents and community in the decision-making processes behind AV deployment and an unjust distribution of costs and benefits. The scientific dialogue on acceptance of AVs needs to shift towards resistance as the ‘other’ essential element of acceptance to ensure that we live up to our promise of transitioning towards more sustainable mobility that is inclusive, equitable, fair, just, affordable, and available to all.

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Tesla's Full Self-Driving Beta (FSD) program introduces technology that extends the operational design domain of standard Autopilot from highways to urban roads. This research conducted 103 in-depth semi-structured interviews with users of Tesla's FSD Beta and standard Autopilot to evaluate the impact on user behavior and perception. It was found that drivers became complacent over time with Autopilot engaged, failing to monitor the system, and engaging in safety-critical behaviors, such as hands-free driving, enabled by weights placed on the steering wheel, mind wandering, or sleeping behind the wheel. Drivers' movement of eyes, hands, and feet became more relaxed with experience with Autopilot engaged. FSD Beta required constant supervision as unfinished technology, which increased driver stress and mental and physical workload as drivers had to be constantly prepared for unsafe system behavior (doing the wrong thing at the worst time). The hands-on wheel check was not considered as being necessarily effective in driver monitoring and guaranteeing safe use. Drivers adapt to automation over time, engaging in potentially dangerous behaviors. Some behavior seems to be a knowing violation of intended use (e.g., weighting the steering wheel), and other behavior reflects a misunderstanding or lack of experience (e.g., using Autopilot on roads not designed for). As unfinished Beta technology, FSD Beta can introduce new forms of stress and can be inherently unsafe. We recommend future research to investigate to what extent these behavioral changes affect accident risk and can be alleviated through driver state monitoring and assistance.

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As the introduction of automated vehicles (AVs) into road traffic accelerates, establishing user acceptance is increasingly crucial. User comfort, largely influenced by the AVs' driving styles, is one of the essential factors influencing acceptance. This video submission provides a methodological overview of a qualitative interview study, which used a Wizard-of-Oz method to investigate participants' comfort levels during automated driving on real roads. By understanding the specific comfort experiences of both older and younger users, we can inform the design process for AVs, thereby enhancing user experience and facilitating broader acceptance of technology across a more diverse and inclusive demographic spectrum.

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The present study surveyed actual extensive users of SAE Level 2 partially automated cars to investigate how driver’s characteristics (i.e., socio-demographics, driving experience, personality), system performance, perceived safety, and trust in partial automation influence use of partial automation. 81% of respondents stated that they use their automated car with speed (ACC) and steering assist (LKA) at least 1–2 times a week, and 84 and 92% activate LKA and ACC at least occasionally. Respondents positively rated the performance of Adaptive Cruise Control (ACC) and Lane Keeping Assistance (LKA). ACC was rated higher than LKA and detection of lead vehicles and lane markings was rated higher than smooth control for ACC and LKA, respectively. Respondents reported to primarily disengage (i.e., turn off) partial automation due to a lack of trust in the system and when driving is fun. They rarely disengaged the system when they noticed they become bored or sleepy. Structural equation modelling revealed that trust had a positive effect on driver’s propensity for secondary task engagement during partially automated driving, while the effect of perceived safety was not significant. Regarding driver’s characteristics, we did not find a significant effect of age on perceived safety and trust in partial automation. Neuroticism negatively correlated with perceived safety and trust, while extraversion did not impact perceived safety and trust. The remaining three personality dimensions ‘openness’, ‘conscientiousness’, and ‘agreeableness’ did not form valid and reliable scales in the confirmatory factor analysis, and could thus not be subjected to the structural equation modelling analysis. Future research should re-assess the suitability of the short 10-item scale as measure of the Big-Five personality traits, and investigate the impact on perceived safety, trust, use and use of automation.@en

Recent research suggests the existence of a general acceptance factor (GAF), similar to the “big one” in personality research or the general intelligence factor (g). The current study, written in the form of a short commentary, sought empirical support for the GAF by using data from a large multinational questionnaire of the L3Pilot project on the acceptance of conditionally automated cars (CACs). Our analysis provides clear support for a GAF of CACs, as this factor explained 55% of the variance among the questionnaire items. Criterion validity was established by demonstrating an inverted U-curve between GAF scores and respondents’ ages in 17 countries. It is recommended that researchers concerned with technology acceptance consider whether their acceptance constructs are sufficiently unique or merely part of a positive manifold.@en

The L3Pilot project tested SAE Level 3 (L3) conditionally automated driving functions addressing driving and travel behavior, impacts on safety, efficiency, environment and socio-economics, and user acceptance. To investigate individual variance in acceptance of conditionally automated cars, an online survey was performed among 18,631 respondents from 17 countries evaluating differences in age, gender, knowledge about the functionality of conditionally automated cars, awareness, information consumption behavior, and expected benefits of conditionally automated cars. Respondents were divided into Enthusiasts, Neutrals, and Sceptics differing in a high, moderate, and low acceptance of conditionally automated cars, respectively. Enthusiasts, Neutrals, and Sceptics differed most with regard to the expected benefits in the productive use of travel time, comfort, and safety of conditionally automated cars. Enthusiasts were male, younger, more knowledgeable about conditionally automated cars, more aware of automated cars, and more likely to receive information about automated cars from different sources, expecting improvements in the productive use of travel time, comfort, and safety due to conditionally automated cars. All groups were most knowledgeable about the lane keeping behavior of conditionally automated cars and least knowledgeable about the operation of conditionally automated cars in dedicated operational design domains. The results indicate that the communication and marketing of automated cars should create a realistic image of the capabilities and limitations of conditionally automated cars where user education programs should be harmonized to calibrate expectations and educate the public.

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The present study investigated the attitudes and acceptance of automated shuttles in public transport among 340 individuals physically experiencing the automated shuttle ‘Emily’ from Easymile in a mixed traffic environment on the semi-public EUREF (Europäisches Energieforum) campus in Berlin. Automated vehicle acceptance was modelled as a function of the Unified Theory of Acceptance and Use of Technology (UTAUT) constructs performance expectancy, effort expectancy, social influence, and facilitating conditions, the Diffusion of Innovation Theory (DIT) constructs compatibility and trialability, as well as trust and automated shuttle sharing. The results show that after adding the DIT constructs, automated shuttle sharing, and trust to the model, the effect of performance expectancy on behavioural intention was no longer significant. Instead, compatibility with current travel was the strongest predictor of behavioural intention to use automated shuttles. It was further found that individuals who are willing to share rides in an automated shuttle with fellow travelers (i.e., automated shuttle sharing) and who trust automated shuttles (i.e., trust) are more likely to intend to use automated shuttles (i.e., behavioural intention). The highest mean rating was obtained for believing that automated shuttles are easy to use, while the lowest mean rating was obtained for feeling safe inside the automated shuttle without any type of supervision. The analysis revealed a preference for the supervision of the automated shuttle via an external control room to the supervision by a human steward onboard. We recommend future research to investigate the hypothesis that compatibility could serve as an even stronger predictor of the behavioural intention to use automated shuttles in public transport than performance expectancy.

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The present online study surveyed drivers of SAE Level 2 partially automated cars on automation use and attitudes towards automation. Respondents reported high levels of trust in their partially automated cars to maintain speed and distance to the car ahead (M = 4.41), and to feel safe most of the time (M = 4.22) on a scale from 1 to 5. Respondents indicated to always know when the car is in partially automated driving mode (M = 4.42), and to monitor the performance of their car most of the time (M = 4.34). A low rating was obtained for engaging in other activities while driving the partially automated car (M = 2.27). Partial automation did, however, increase reported engagement in secondary tasks that are already performed during manual driving (i.e., the proportion of respondents reporting to observe the landscape, use the phone for texting, navigation, music selection and calls, and eat during partially automated driving was higher in comparison to manual driving). Unsafe behaviour was rare with 1% of respondents indicating to rarely monitor the road, and another 1% to sleep during partially automated driving. Structural equation modeling revealed a strong, positive relationship between perceived safety and trust (β = 0.69, p = 0.001). Performance expectancy had the strongest effects on automation use, followed by driver engagement, trust, and non-driving related task engagement. Perceived safety interacted with automation use through trust. We recommend future research to evaluate the development of perceived safety and trust in time, and revisit the influence of driver engagement and non-driving related task engagement, which emerged as new constructs related to trust in partial automation.

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The study investigated the interrelationships between the UTAUT2 (Unified Theory of Acceptance and Use of Technology) constructs predicting intentions to use driverless automated shuttles. Survey data was gathered from individuals physically experiencing an automated shuttle in a mixed traffic environment on public roads in Trikala (Greece) as part of the CityMobil2 project. Structural equation modelling showed that intentions to use automated shuttles were most strongly driven by hedonic motivation, followed by performance expectancy and social influence. Performance expectancy mediated the relationship between effort expectancy and behavioural intention. This means that people's expectations of how difficult automated shuttles will be to use and their intentions to use them can be explained through their expectations around how these vehicles will perform. Technology savviness was a negative moderator of the relationship between social influence and performance expectancy and facilitating conditions, respectively. Tech-savvy individuals rely less on their social networks to nurture their beliefs that automated shuttles are useful and to have the necessary resources to use automated shuttles. Car use was a negative predictor of the intention to use automated shuttles. Future research should revisit the interrelationships between the UTAUT2 constructs and apply (quasi-) experimental studies to unravel the temporal interaction between constructs.Relevance to human factors/Relevance to ergonomics theoryThe study investigated the interrelationships between the UTAUT2 (Unified Theory of Acceptance and Use of Technology) constructs predicting intentions to use driverless automated shuttles. Survey data from individuals physically experiencing an automated shuttle in Trikala (Greece) as part of the CityMobil2 project was analysed by structural equation modeling. Examining the interrelationships between the UTAUT2 constructs enables us to identify underlying beliefs and devise adequate strategies to promote automated vehicle acceptance. Supplemental data for this article is available online at https://doi.org/10.1080/1463922X.2020.1814446.

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A necessary condition for the effective integration of automated vehicles in our daily lives is their acceptance by passengers inside and pedestrians and cyclists outside the automated vehicle. 119 respondents experienced an automated shuttle ride with a ‘hidden steward on board’ in a mixed traffic environment in Berlin-Schöneberg. A mixed-method approach was applied gathering qualitative interview data during the ride and quantitative questionnaire data after the ride. Responses were classified into three main categories: (1) Perceived safety, (2) interactions with automated shuttles in crossing situations, and (3) communication with automated shuttles. Respondents associated their perceptions of safety with the low speed, dynamic object and event identification, longitudinal and lateral control, pressing the emergency button inside the shuttle, their general trust in technology, sharing the shuttle with fellow travellers, the operation of the shuttle in a controlled environment, and the behaviour of other road users outside the shuttle. Respondents pressed the emergency button inside the automated shuttle on 28 out of 62 test rides in order to test its behavior. They further expected to be more cautious in crossing the road before an automated shuttle due to the lack of eye contact with the human driver and a lack of trust in the behavior of the automated shuttle, and expected road users testing the automated shuttle due to the conservative driving behavior of automated shuttles. We recommend future research into the hypothesis that the acceptance of automated shuttles will be associated with the perceived safety of and their effective and intuitive interaction and communication with both passengers and other road users.

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The acceptance of automated vehicles is a necessary condition for realizing the benefits of road vehicle automation. The objective of the thesis was to examine the acceptance of automated vehicles feeding public transport. The thesis investigated the factors contributing to user acceptance, as well as the interrelations of those factors. It was also investigated how acceptance differs across socio-demographic groups and countries. Online questionnaires, semi-structured interviews, a systematic literature review, and accompanied test rides were performed. Participants were asked to imagine the use of automated vehicles (Chapter 2) or physically experienced them in mixed traffic environments in Berlin (Germany) and Trikala (Greece) (Chapters 3–5, 7–8)....@en

We investigated public acceptance of conditionally automated (SAE Level 3) passenger cars using a questionnaire study among 9,118 car-drivers in eight European countries, as part of the European L3Pilot project. 71.06% of respondents considered conditionally automated cars easy to use while 28.03% of respondents planned to buy a conditionally automated car once it is available. 41.85% of respondents would like to use the time in the conditionally automated car for secondary activities. Among these 41.85%, respondents plan to talk to fellow travellers (44.76%), surf the internet, watch videos or TV shows (44%), observe the landscape (41.70%), and work (17.06%). The UTAUT2 (Unified Theory of Acceptance and Use of Technology) was applied to investigate the effects of performance and effort expectancy, social influence, facilitating conditions, and hedonic motivation on the behavioural intention to use conditionally automated cars. Structural equation analysis revealed that the UTAUT2 can be applied to conditional automation, with hedonic motivation, social influence, and performance expectancy influencing the behavioural intention to buy and use a conditionally automated car. The present study also found positive effects of facilitating conditions on effort expectancy and hedonic motivation. Social influence was a positive predictor of hedonic motivation, facilitating conditions, and performance expectancy. Age, gender and experience with advanced driver assistance systems had significant, yet small (<0.10), effects on behavioural intention. The implications of these results on the policy and best practices to enable large-scale implementation of conditionally automated cars on public roads are discussed.

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In the future, automated shuttles may provide on-demand transport and serve as feeders to public transport systems. However, automated shuttles will only become widely used if they are accepted by the public. This paper presents results of an interview study with 30 users of an automated shuttle on the EUREF (Europäisches Energieforum) campus in Berlin-Schöneberg to obtain in-depth understanding of the acceptance of automated shuttles as feeders to public transport systems. From the interviews, we identified 340 quotes, which were classified into six categories: (1) expectations about the capabilities of the automated shuttle (10% of quotes), (2) evaluation of the shuttle performance (10%), (3) service quality (34%), (4) risk and benefit perception (15%), (5) travel purpose (25%), and (6) trust (6%). The quotes indicated that respondents had idealized expectations about the technological capabilities of the automated shuttle, which may have been fostered by the media. Respondents were positive about the idea of using automated shuttles as feeders to public transport systems but did not believe that the shuttle will allow them to engage in cognitively demanding activities such as working. Furthermore, 20% of respondents indicated to prefer supervision of shuttles via an external control room or steward on board over unsupervised automation. In conclusion, even though the current automated shuttle did not live up to the respondents’ expectations, respondents still perceived automated shuttles as a viable option for feeders to public transport systems.@en

If automated vehicles (AVs) are to move efficiently through the traffic environment, there is a need for them to interact and communicate with other road users in a comprehensible and predictable manner. For this reason, an understanding of the interaction requirements of other road users is needed. The current study investigated these requirements through an analysis of 22 h of video footage of the CityMobil2 AV demonstrations in La Rochelle (France) and Trikala (Greece). Manual and automated video-analysis techniques were used to identify typical interaction patterns between AVs and other road users. Results indicate that road infrastructure and road user factors had a major impact on the type of interactions that arose between AVs and other road users. Road infrastructure features such as road width, and the presence or absence of zebra crossings had an impact on road users’ trajectory decisions while approaching an AV. Where possible, pedestrians and cyclists appeared to leave as much space as possible between their trajectories and that of the AV. However, in situations where the infrastructure did not allow for the separation of traffic, risky behaviours were more likely to emerge, with cyclists, in particular, travelling closely alongside the AVs on narrow parts of the road, rather than waiting for the AV to pass. In addition, the types of interaction varied considerably across socio-demographic groups, with females and older users more likely to show cautionary behaviour around the AVs than males, or younger road users. Overall, the results highlight the importance of implementing the correct infrastructure to support the safe introduction of AVs, while also ensuring that the behaviour of the AV matches other road users’ expectations as closely as possible in order to avoid traffic conflicts.

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